Model Configuration

The regional climate model
we present here is a limited area model, and thus must receive forcing data, i.e.
initial and boundary conditions,
from another source model. First we summarize these large-scale simulations and
follow with a detailed discussion of the regional model configuration.

A.Forcing data

Reanalysis simulations
with a general circulation model (GCM) provide an excellent forcing dataset for
validating the regional model. Reanalysis grids are similar to free-running
global models in terms of spatial resolution. However, since observations are
assimilated into the simulation, simulated large-scale fields such as
temperature, geopotential heights, and humidity are closely constrained to the
actual atmospheric state. Thus, the output fields represent an idealize GCM in
which the large-scale atmospheric state and its time evolution is well
represented on the daily, seasonal, and interannual scale. Regional simulations
forced by reanalysis fields help isolate deficiencies in the regional model
without the complexity of biases inherited from the forcing model. Here, we use
the NCEP/NCAR Reanalysis Project (NNRP) data {Kalnay 1996} for the 10-year
period 1990-1999. Data are simulated at 6-hourly intervals, 2.5 degree x 2.5
degree horizontal resolution (approx. 275 km x 200 km), and 17
pressure levels.

We also present here
simulations forced by two free-running global climate models, NCAR/DOE
Parallel Climate Model (PCM) and ECHAM5. The ECHAM5 results are also used for
climate change simulations in a companion paper {Salathé, 2007} and will be the
main focus of the present paper. PCM is a global model derived from the
National Center for Atmospheric Research (NCAR) Climate System Model with a
parallel ocean model developed at the Department of Energy (DOE); the
simulation used here was run at T42 resolution (approx 220x300‑km grid spacing). ECHAM5 is based on the
fifth-generation atmospheric general circulation model developed at the Max
Planck Institute for Meteorology (ECHAM5). This model is the most recent
version in a series of ECHAM models evolving from the spectral weather
prediction model of the European Centre for Medium Range Weather Forecasts
(Roeckner et al., 2003). ECHAM5
was coupled to the Max Planck Institute ocean model (MPI-OM) and was forced for
the current study with observed radiative parameters. ECHAM5 was run at T63
spectral resolution, which corresponds to a horizontal grid spacing of
approximately 140x210 km grid spacing at mid-latitudes.

Output from the forcing
model was used to provide initial and lateral boundary conditions every six
hours for the regional climate model. The global model output was also used to nudge
the outermost regional model domain, which is discussed in detail below.
Regional simulations will be referred to as NNRP-MM5, PCM-MM5, and ECHAM5-MM5
to indicate the forcing model.

B.Regional Model

The Pennsylvania State University
(PSU)-National Center for Atmospheric Research (NCAR) mesoscale model (MM5)
Release 3.6 was used as the regional climate model. Although MM5 was developed
for mesoscale weather forecasting and has been operating as such in real-time
at the University of Washington (UW) and many other places for over a decade,
there is precedent for its use as a tool for regional climate modeling {Leung,
2004 #583}. MM5 is a limited-area, non-hydrostatic, terrain-following
sigma-coordinate model designed to simulate or predict mesoscale atmospheric
circulation {Grell, 1993 #558}. Parameterizations include Kain-Fritsch
convective parameterization {Kain, 1993 #557}, Medium Range Forecast model
(MRF) planetary boundary layer (PBL) scheme {Hong, 1996 #570}, CCM2 radiation
scheme {Hack, 1993 #559}, and Simple Ice cloud microphysics {Dudhia, 1989 #572}.

High regional model
resolution is achieved by using multiple MM5 nests at 135 km, 45 km, and 15 km
horizontal grid spacing. Figure 4 shows the MM5 nests used in this study. It is
worth noting that “one-way” nesting is utilized; that is, the global climate
model is run independently first, without updates from the regional model
solution; furthermore, information only passes from the outer to inner nests in
the mesoscale simulation.

In order to capture the
large-scale processes important for Pacific Northwest climate outermost MM5
domain encompasses nearly the entire North American continent and much of the
eastern Pacific Ocean. The use of such a large outer domain keeps the outer
mesoscale boundaries far from region of study and weather systems approaching
the Pacific Northwest well represented by the time they reach the region. The
second nest covers the western United States and portions of Canada and Mexico,
capturing storm systems and Southwest Monsoon circulations that influence the
Pacific Northwest. The innermost domain covers the states of Washington,
Oregon, and Idaho and the entire Columbia River Basin.

1.Nudging

As with the MM5-based
real-time numerical weather forecasting system used at the University of
Washington {cite}, nudging is applied to the outermost regional model domain
from the forcing fields. Nudging relaxes the regional model solution for wind,
temperature, and moisture towards the driving global climate model solution.
The relaxation takes place throughout the interior of the domain and at all
vertical levels above the planetary boundary layer. Particularly over large
domains, the regional model solution can drift over time from that of the
driving global climate model. If we assume that the global climate model
reasonably captures synoptic-scale structures and that the goal of the dynamic
downscaling system is simply to obtain fine-scale detail for a given
large-scale pattern, then the regional model should not modify the large-scale
patterns, and such a drift is undesirable.

Other methods to limit
this drift rely on using smaller regional model domains (Jones et al., 1995) or periodic (e.g. every 10 days) reinitializations of the simulation
(Pan et al. 1999). Since some
spin up time is required after each reinitializations, this approach is
computationally inefficient. But more importantly, reinitializing the model
looses the slow varying parameters in the model, such as soil parameters and
snow cover, that are the essence of climate system modeling. Von Storch et
al. (2000) showed that nudging was
able to keep simulated states close to the driving state at large scales while
still generating small-scale features. The development of nudging has yielded
an option that allows for a larger regional model domains and makes continuous
model runs possible but still limits model drift.

The inner two domains are
not nudged, allowing the mesoscale model to freely develop atmospheric
structures at finer spatial scale. This approach attempts to preserve the
large-scale state provided by the global model while generating regional
meteorological details on the inner nests.

2.Soil Parameterization

Accurate representation of
land-atmosphere interactions in climate models is critical to the realistic
simulation of global and regional energy and water cycles (Wang et al., 2004). Realistic modeling of soil moisture and
temperature dynamics, in particular, is crucial in capturing moisture and heat
fluxes at the surface. These physical processes, in turn, directly influence
air temperature, air moisture, and snow dynamics. Snow pack in particular is
critical for understanding climate impacts in regions such as the Pacific
Northwest where snow melt plays a central role in regional hydrology. In order
to capture these dynamics in the climate system over climate change scales, the
soil column must freely interact with the atmosphere. Most climate models,
however, prescribe the lower-boundary soil temperature to some climatological
value, which restricts the response of the soil column to climate forcing.
Furthermore, if the prescribed value is not realistic for the simulated
climate, a spurious heat source or sink is introduced to the land surface.

The upper few meters of
soil act as a heat reservoir, storing heat in the spring and summer and
releasing it in autumn and winter (De Vries, 1975). Heat is transferred through
the soil column primarily by conduction, penetrating a few centimeters to
perhaps half a meter on daily timescales and to depths as large as 10 meters on
annual timescales (De Vries, 1975). Observations (Baxter, 1997) and models
(Jury, Gardner, and Gardner, 1991) of soil temperature evolution at different
soil depths show that soil temperature should be both time-lagged and
amplitude-damped with depth with respect to the annual surface soil cycle. In
these studies, at a depth of three meters (lower soil boundary in the NOAH
LSM), the annual soil temperature cycle is typically time-lagged by 70 days and
amplitude-damped to about one-third the amplitude of the surface temperature
cycle. Thus, we expect the lower boundary of the soil model would interact with
the climate – both on the annual scale and under climate change scenarios. A
prescribed lower boundary temperature at 3 meters depth may not be problematic
for weather forecasting, typically over time scales of days to weeks, since it
takes on the order of months for thermal information at 3m depth to reach the
surface. For climate simulations over many years, however, this deficiency on
the surface energy budget would accumulate large errors in the surface
parameters.

To address these issues,
we have implemented a deep soil temperature parameterization that yields an
annual cycle of deep soil temperature that simulates the observed temperature
cycles deep in the soil column. The soil temperature at depth follows the
variations in the surface skin temperature, but with a phase lag and
attenuation with depth. The phase lag and attenuation depend on the frequency
of the surface variation, but we shall base our methodology on the annual
cycle. The desired response may be obtained by taking a simple weighted average
of the skin temperature over the previous year, where the weighting is adjusted
to yield the desired attenuation and phase lag. We choose a weighting function
with two parameters as follows. We take the mean skin temperature over the full
year () and the n days () prior to the time of interest. The soil temperature
is then the weighted mean of these two values,By selecting appropriate values of a an n we can tune this equation to produced the desired
attenuation and phase lag for the depth of interest. For 3‑m depth, we use the published observed values of 30%
attenuation and 70 days lag to obtain a=0.6 and n = 140 days.

To test this method, we
used surface and 1-m deep soil temperatures observed at Ames Iowa, US. For this
case, we use =0.3 and n = 46 days
to obtain the observed lag and attenuation. Note that, compared to the values
for 3‑m depth, a smaller n yields a smaller lag and the smaller a yields less
attenuation. In figure 10, the blue line shows the observed skin temperature
and the black line the observed 1-m soil temperature. The weighted mean of the
skin temperature yields the red line, which closely captures the form of the
observed soil temperature over four seasonal cycles. Note also how the
parameterization effectively removes the high-frequency variations at the
surface in accordance with observations.

When applied to the MM5
climate modeling system, the parameterization uses surface skin temperatures
generated by MM5 and the NOAH LSM to derive the lower boundary temperature. For
the first year of each decade-long simulation, however, MM5 output is not
available, and must be derived from a spin up simulation (i.e. a preliminary simulation not used in the analysis of
results, but only to equilibrate model parameters). This spin-up is also
required to bring the NOAH LSM soil temperature and moisture fields for the
entire soil column into equilibrium with the simulated atmospheric state.
Studies (Cosgrove et al., 2003,
for example) have shown that, especially in drier regions, a spin-up period of
at least a year is necessary for soil moisture, as the land surface model
slowly adjusts soil parameters away from default values (which can be
unrealistic). Thus, the spin-up simulations are initialized at the end of the
summer (September 1989), when soils are climatologically most dry in the
Pacific Northwest. For simplicity, we use the same forcing data for the spin-up
as for the first year of the actual climate simulation. At initialization, the
global forcing model (i.e. NNRP
OR ECHAM5) is used in Eqn. (1) to set the boundary temperature. The deep soil
temperature parameterization was implemented at each internal time step to
avoid biases in temperature that would result from a once-daily algorithm
implementation. Soil temperatures for all intervening layers (between the
surface and three meters) in the NOAH-LSM are linearly interpolated and then
allowed to evolve according to the LSM throughout the simulation. Soil moisture
is initialized to the climatological values contained in MM5 and then allowed
to evolve over the spin-up year according to the LSM. As MM5 surface data
became available at each time step, the parameterization was updated at each
grid point using the available MM5 output, thereby phasing out the global model
data. At the completion of the spin-up year, a complete year of MM5-derived
skin temperatures is available for the deep soil parameterization and the soil
temperature and moisture profile has spun up to the atmospheric forcing. Figure
11 illustrates the soil temperature for the surface down to three meters for
spin up and first two years of the PCM-MM5 simulation interpolated to the
SeaTac Airport meteorological station. Due to the gradual phase-in of MM5 data
over the spin-up year, a difference between values for the deep (300cm) soil
temperature cycle over the first year and subsequent years is noticeable.

This soil temperature
parameterization not only yields a deep soil temperature cycle that is more realistic
– with amplitude that is significantly damped – but also allows for a change in
the deep soil temperature pattern over time. That is, whereas the default deep
annual temperature cycle is the same year after year, this system allows the
temperature at three meters to evolve with the rest of the climate system.
Thus, when a change in atmospheric radiative forcing occurs and climate change
results, the entire soil column will respond accordingly instead of being
constrained at depth by the same annual cycle year after year.